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Abstract

Background

Despite numerous studies evaluating the benefits of Helicopter Emergency Medical Services
(HEMS) in primary scene responses, little information exists on the scope of HEMS
activities in Australia. We describe HEMS primary scene responses with respect to
the time taken, the distances travelled relative to the closest designated trauma
hospital and the receiving hospital; as well as the clinical characteristics of patients
attended.

Methods

Clinical service data were retrospectively obtained from three HEMS in New South Wales
between July 2008 and June 2009. All available primary scene response data were extracted
and examined. Geographic Information System (GIS) based network analysis was used
to estimate hypothetical ground transport distances from the locality of each primary
scene response to firstly the closest designated trauma hospital and secondly the
receiving hospital. Predictors of bypassing the closest designated trauma hospital
were analysed using logistic regression.

Results

Analyses included 596 primary missions. Overall the HEMS had a median return trip
time of 94min including a median of 9min for activation, 34min travelling to the scene,
30min on-scene and 25min transporting patients to the receiving hospital. 72% of missions
were within 100km of the receiving hospital and 87% of missions were in areas classified
as ‘major cities’ or ‘inner regional’. The majority of incidents attended by HEMS
were trauma-related, with road trauma the predominant cause (44%). The majority of
trauma patients (81%) had normal physiology at HEMS arrival (RTS = 7.84). We found
62% of missions bypassed the closest designated trauma hospital. Multivariate predictors
of bypass included: age; presence of spinal or burns trauma; the level of the closest
designated trauma hospital; the transporting HEMS.

Conclusion

Our results document the large distances travelled by HEMS in NSW, especially in rural
areas. The high proportion of HEMS missions that bypass the closest designated trauma
hospital is a seldom mentioned benefit of HEMS transport. These results along with
the characteristics of patients attended and the time HEMS take to complete primary
scene responses are useful in understanding the benefit HEMS provides and the services
it replaces.

Keywords:

Background

In New South Wales (NSW) Australia, Helicopter Emergency Medical Services (HEMS) undertake
primary scene responses and secondary inter-facility transfers as part of the state
trauma plan and critical care networks
[1,2].

Compared to road transport systems, there are three predominant advantages of using
HEMS for a primary scene response. They include faster transport to definitive care,
access to patients where limited infrastructure precludes timely road access, and
direct delivery to the patient of advanced life-saving critical skills by a specialty
trained physician or paramedic. During patient transport, a further advantage of HEMS
is the ability to bypass regional hospitals and transfer patients directly to hospitals
which have appropriate facilities, as per local trauma treatment guidelines
[2]. In NSW, a HEMS primary response is recommended in scenarios including difficult
patient access (e.g. cliff fall or water rescue), where the patient condition requires
specialised interventions (e.g. rapid sequence intubation) or situations where a helicopter
will provide a more expedient response and transport
[3,4]. In spite of its high costs, recent evidence indicates that HEMS are potentially
cost-effective
[5] but this is dependent on the accuracy of triage
[6].

To understand the role of HEMS as a distinct intervention in primary scene responses,
it is necessary to evaluate the effect of HEMS on patient outcome, compared to conventional
ground transport
[7-11]. However there are many challenges to such studies as HEMS encompass many aspects
such as decreasing the time to definitive care (logistics), and rapidly providing
potentially life-saving critical interventions. Both within and between jurisdictions,
HEMS are also known to vary in aspects such as staffing and skill levels, range of
operations (e.g. pre-hospital care, inter-hospital retrieval and SAR) and the types
of patients attended. Therefore, to understand how a HEMS may benefit patients from
primary scene responses in NSW, a fundamental step is to provide an understanding
of HEMS operations with respect to the key time performance indicators, the proximity
of operations to the destination hospital and the types of the patients attended.
Further, although HEMS are known to travel large distances, the proportion of missions
which bypass closer designated trauma hospitals has not been previously estimated
in NSW.

Aim

The aim of this study was to document the scope of HEMS primary scene responses in
NSW with respect to the time taken, the distances travelled and the clinical characteristics
of patients attended. Additionally, this study examines the proportion of missions
which bypass the closest designated trauma hospital and the predictors of hospital
bypass.

Methods

Setting

The state of NSW is situated on the east coast of Australia and is characterized by
a large land mass (over 800,000 Square Km) and a population of approximately 6.8 million
people, who predominantly reside near coastal areas. The capital city is Sydney which
incorporates approximately two thirds of the population of NSW (approximately 4.5
million). As of the 1st July 2008, the NSW trauma care system incorporated a networked
system of 23 designated trauma hospitals, which were classified as either major adult
(n=9), major paediatric (n=3), regional (n=2) or rural regional (n=10) according to
available resources
[2]. During this time, nine HEMS operated in NSW, performing primary scene responses
and secondary inter-facility transfers as part of the NSW trauma system. HEMS are
activated by service protocols according to MIST criteria (Mechanism of injury; Injuries
sustained; physiological Signs and symptoms; Transport time) or via a rapid launch
coordinator. Three HEMS are located in the Sydney metropolitan and the remaining six
HEMS are located in regional areas of NSW
[12]. One of the metropolitan services operated a separate rapid response trauma-only
service as part of an ongoing clinical trial
[13].

Data collection

This study was approved by the Sydney South West Area Health Service HREC. We performed
a retrospective cross-sectional analysis of clinical service data for primary scene
responses collected by three HEMS (Greater Sydney Area HEMS) in NSW for the period
1st July 2008 through 30th June 2009 operated by the Ambulance Service of NSW.

Clinical service and key timing data are collected by each HEMS service in NSW for
each patient attended to by a HEMS crew. Data are usually recorded by the medical
crew during the mission and then transcribed contemporaneously onto case sheets. These
are entered into a database following the mission. Data fields include date of transport;
components of transport time (e.g. time trip is activated; time arrived at patient);
mission type (primary scene response or secondary inter-facility transport); patient
diagnosis categories; transport origin and destination; and clinical interventions
undertaken by HEMS staff. Patient clinical observation data are also collected from
time of patient contact until stretcher offload on completion of the mission.

Of the nine HEMS operating in NSW during the reference period, consistent clinical
service data for primary scene responses were available in four HEMS only. This included
two HEMS based in the Sydney metropolitan area (Metro1 & Metro2) and two HEMS located
in regional population centres (Regional1 and Regional2). Three HEMS were operated
by the same provider (Greater Sydney Area HEMS). One of the metropolitan HEMS (Metro2)
with consistent data was operated by Careflight Ltd. During the study period, the
CareFlight HEMS was solely operating within the confines of a head injury trial (HIRT)
and was therefore excluded.

For each HEMS primary scene response we estimated a corresponding hypothetical ground
transport distance, using a Geographic Information Systems (GIS) based network analysis.
Response locations were mapped using Google Earth and imported into the GIS. A road
network layer was compiled using GEODATA TOPO 250k Map Series (Geoscience Australia)
to model vehicle transport routes. The ArcGIS 9.3.1 Network Analysis extension was
used to model the travel distance between each incident location and firstly the nearest
designated trauma hospital and secondly the receiving trauma hospital based on the
optimal travel route. In identifying optimal travel routes, travel impedance factors
(e.g. gravel roads) were established to account for variability in travel speed associated
with the road type.

To assess the ‘remoteness’ of the populations serviced by each HEMS we used the enhanced
Accessibility/Remoteness Index of Australia (ARIA+) score
[14]. The ARIA+ scores localities according to their proximity to service centres using
a continuous scale from 0 (high accessibility) to 15 (high remoteness). Scores can
be further classified into 5 major categories: major cities, inner regional, outer
regional, remote and very remote. We recorded the proportion of responses in each
ARIA+ category according to the postcodes attended by each HEMS during the 2008/2009
financial year.

Data analysis

Data were analysed using SAS version 9.2. For the analysis of HEMS time and distance
we included only primary scene responses categorised as ‘emergency’ as these correspond
to time critical missions that require a rapid response. We also excluded missions
requiring a winch as we assumed these missions can only be completed by HEMS (and
ground distance was unable to be calculated). For each HEMS primary scene response
the following times were calculated: activation time (time between activation call
received and helicopter departure from base to the scene), response time (time between
departure from base to arrival at scene), scene time (time between arriving at the
patient and departing the scene for destination hospital), transport time (time between
departing the scene and arriving at destination hospital) and total time (time between
activation call received and arriving at destination hospital).

We described patient demographics, clinical characteristics and diagnostic groups
for all primary scene responses. We defined trauma patients according to the incident
recorded in the service database, such as ‘motor vehicle accident’ or ‘fall’. For
trauma patients, we described the types of trauma sustained (such as chest or head
trauma) according to the APACHE III sub-diagnosis recorded in the HEMS database
[15]. For non-trauma patients, diagnoses were described according to the APACHE III titles
(such as cardiovascular or metabolic).

Continuous variables including age, Glasgow Coma Score (GCS)
[16] and Revised Trauma Score (RTS)
[17] were categorised according to standard definitions. The association between potential
predictors such as type of trauma sustained and whether or not patients were taken
to the closest designated trauma hospital were analysed using stepwise logistic regression.
Each potential predictor was firstly examined in a univariate model and factors with
a predetermined level of significance (p<0.1) were then entered into a multivariate
model.

Results

During the reference period, a total of 596 primary scene responses were identified
from clinical service data in the three HEMS. The metropolitan HEMS undertook more
missions (N=411, 69%) than the Regional HEMS (Regional1 N=102, 17%; Regional2 N=83,
14%). After excluding non-emergency missions and winch missions, 464 primary scene
responses were used to calculate distance and time (78% of total missions [Metro1
HEMS N=308, 66%; Regional1 HEMS N=74, 16%; Regional2 HEMS N=82, 18%]).

Table
1 shows the mean and median time taken for the HEMS non-winch and emergency responses
(N=464). Overall, a return trip took a median time of 94min. Based on the inter-quartile
range of each time component, between 5%-11% of time was spent in activation, between
27%-48% was spent travelling to the scene, between 21%-43% was spent on-scene and
between 27%-37% was spent transporting the patient to the receiving hospital.

Our data allowed us to estimate the road distance from the response location to the
nearest designated trauma hospital and the actual receiving hospital for 425 (92%)
and 406 (88%) primary scene responses respectively (Figure
1). For the 406 missions in which we had information on the receiving hospital, we
found HEMS bypassed the closest designated trauma hospital in 62% of cases (N = 406).

Figure 1.Map of NSW including remoteness index (ARIA+), trauma services and location of primary
scene responses during July 2008 to June 2009.

Table
2 shows the patient and HEMS characteristics that were associated with bypassing the
closest designated trauma hospital. In univariate analyses we found classification
of spinal or burns trauma, along with age, GCS, the level of closest designated trauma
hospital and the service all met the pre-defined level of significance (P<0.1) and
were therefore included in a multivariate model. After adjusting for all factors in
the multivariate analysis, results showed patients with spinal or burns trauma were
less likely to be taken to the closest hospital compared to patients without both
these types of trauma (OR: 0.47 p=0.055 [spinal]; OR: 0.13 p=0.046 [burns]). Regarding
age, results showed paediatric patients (<=16 years) were less likely to be taken
to the closest hospital compared to adult patients (OR: 0.48; p=0.042). We also found
the level of closest designated trauma hospital was a highly significant predictor
of bypass (p<0.0001) with patients less likely to be taken to the closest hospital
if it was classified as regional (OR: 0.16) and rural regional (OR 0.09) compared
to hospitals classified as major trauma. Finally, the individual HEMS transporting
the patient also predicted hospital bypass with both Regional1 and Regional2 more
likely to take patients to the closest designated trauma hospital (OR: 3.61 & 10.95;
p<0.0001 & 0.001 respectively) compared to Metro1.

Table 2.Univariate and multivariate predictors of HEMS transports being taken to the closest
designated trauma hospital

Figure
2 shows that the metropolitan HEMS transported the majority of their patients to a
hospital within 100km of the scene (Metro1: 85%), where in contrast, the regional
HEMS transported a higher proportion of patients to a hospital that was greater than
100km from the scene (Regional1: 46%; Regional2: 63% respectively). Metro1 travelled
a median distance of 44km (IQR: 24-78km) to the receiving hospital whereas Regional1
and Regional2 travelled median distances of 94km (IQR: 54-131km) and 114km (IQR: 83-180km)
to the receiving hospital respectively.

Figure
3 provides an overview of the remoteness of HEMS activities. Primary scene responses
for the metropolitan HEMS were predominately classified as either ‘major cities’ or
‘inner regional’. In contrast, Regional2 HEMS responded to a majority of localities
classified as ‘outer regional’ or ‘inner regional’.

Table
3 provides an overview of all patients attended by HEMS (including non-emergency and
winch missions; N=596). The majority of patients were male (n=434, 74%) and adults
with 13% (N=78) aged over 60 (median age: 37; IQR: 20–51). In terms of primary scene
responses attended by HEMS, most were trauma related (N=555, 93%) with road trauma
the predominant cause of trauma-related incidents (N=259, 47%). For trauma patients,
injuries to the extremities were the most common (N=238, 31%).

Table
3 also shows patient clinical condition as judged by the attending clinician as well
as patient physiology at arrival. We found 2% of patients (N=9) were dead on arrival
of the HEMS team with a further 2% (N=11) dying post-arrival or en-route to hospital.
The majority of patients were considered stable at arrival (N=474, 80%), with the
majority of these patients remaining stable (N=466, 98% of initially stable patients).
Seventeen percent of patients (N=101) were considered unstable at arrival. For trauma
patients (excluding patients who were dead on arrival), we found 73% of patients had
a normal GCS score of 15 (N=394). We were also able to calculate the Revised Trauma
Score for 486 patients (88% of trauma patients) with 81% of these patients recording
a normal score of 7.84 (N=394).

Table
4 shows patient characteristics in relation to the clinical condition at arrival. For
patients that died before or after the HEMS arrival or en-route to the hospital, we
found a high proportion of non-trauma incidents (33% and 46% respectively). The majority
of these non-trauma responses were classified as due to cardiovascular events (22%
and 36% respectively) such as cardiac arrest.

Table 4.Characteristics of patients stratified by clinical condition on arrival at the scene

Discussion

To date, this is the most comprehensive description of both metropolitan and regional
HEMS primary scene responses in NSW which includes the time taken and the proximity
of missions to the receiving hospital. Our results highlight the often large distances
travelled by HEMS in NSW in transport to the receiving hospital. During patient transport,
HEMS in NSW often bypass the nearest designated trauma hospital as part of the local
regionalised trauma care system. Patients transported by HEMS during primary scene
responses were predominantly judged to be clinically stable.

Around the world, HEMS operate in many different settings including urban, rural and
remote environments. We found HEMS in NSW predominantly operated in areas classified
as ‘major cities’ or ‘inner regional’ although differences existed between the urban
and regional based HEMS. For HEMS in regional areas, we found on average a two-fold
increase in the average distance travelled relative to the trauma hospital, compared
to the metropolitan HEMS. Such differences suggest variance in the benefits of HEMS
in NSW which includes improving health service equity in regional areas and providing
a ‘second tier’ of support in urban areas. Locations of HEMS in NSW are historically
determined and given the large distances travelled by HEMS in NSW, further research
is needed into the most appropriate HEMS locations relative to need.

Our results regarding distances travelled by HEMS in regional areas are consistent
with previous research in NSW
[18]. Compared to a meta-analysis from the US of HEMS pre-hospital care times for trauma
[19], our results showed HEMS in NSW have longer times in all categories. This discrepancy
may reflect several features of the local system such as the large distances travelled
to the scene and the use of physicians as opposed to paramedics (which are predominantly
used in US HEMS). A recent analysis from California showed over 60% of HEMS primary
scene responses were within 29miles (~47km) of the receiving hospital
[20]. This compared to approximately 40% of missions within the same distance in our analysis.
Compared to European HEMS, which are predominantly physician staffed, our NSW transport
times were also longer with HEMS transport times to the receiving hospital in the
Netherlands reported as a mean of 13min
[21]. This compared to a mean of 30min in our study.

Our results also showed HEMS in NSW attend a large diversity of trauma incidents including
road trauma, falls, sports injuries and a small proportion of patients classified
as non-trauma. The predominance of road trauma reported in our study is similar to
previous findings in other jurisdictions
[22-24] although the proportion of incidents was slightly less than that previously reported
in NSW
[18,25]. The majority of patients attended by HEMS were assessed to be clinically stable
and had normal physiology, although data limitations precluded a true assessment of
illness severity. Previous research has documented high over-triage rates in HEMS
primary scene responses
[26]. Given the expense of HEMS in NSW
[12], there is scope for further research into the accuracy of current dispatch criteria
in NSW to ensure HEMS are targeted to appropriate patients.

An important finding in our study was the high proportion of missions that bypassed
the closest designated trauma hospital. This highlights a seldom-mentioned advantage
of HEMS which incorporates the crew’s ability to exercise clinical judgment and take
patients to appropriate hospitals without being restricted by road networks or travel
time. In practice this includes burns and spinal trauma as well as paediatric patients
being transferred to specialised hospitals where definitive specific care can be provided.
HEMS also bypassed lower grade trauma hospital in order to take patients to major
trauma hospitals that can provide clinical services such as interventional radiology,
cardiothoracic surgery and neurosurgery. We also noted a lower probability of hospital
bypass by regional HEMS, which may have reflected patients who do not require neurosurgery
or cardiothoracic surgery being taking to regional trauma services, as would be appropriate.
More broadly, the high proportion of HEMS bypass reflects HEMS in NSW functioning
as part of a regionalised trauma care system that has been shown to reduce mortality
[27,28].

Previous studies evaluating the effect of HEMS on patient mortality, have predominantly
compared HEMS to a direct scene transport via ground
[7,8]. In the NSW jurisdiction, our findings regarding distances travelled and the frequency
of hospital bypass, highlight that HEMS are likely to replace ground transport to
a regional hospital in some instances. Depending on patient acuity, this may be followed
by stabilisation and subsequent transport to a major trauma hospital. Hence, when
examining the economics of HEMS transports, future studies need to consider the appropriate
alternative to HEMS and the full “opportunity cost” of withholding HEMS.

Limitations

Due to inconsistent data collection we were unable to include four ‘regional’ HEMS
which undertake primary scene responses in NSW in this analysis. In future this limitation
will be addressed through the introduction of a state wide uniform HEMS database (Air
Maestro). The HEMS that we were able to include are representative of both metropolitan
and regional HEMS activities and together are responsible for approximately half of
the total HEMS primary scene response activities in NSW
[12]. The service excluded from our study (Metro2), operated in predominantly urban areas
for the sole purpose of a clinical trial of rapid responses to head injured patients.
Although by omitting this service the reported number of patients attended by HEMS
in the study period with head injuries and (other factors) is likely to be underestimated,
the unique nature of the Metro2 service during the study period meant the data would
not be representative of traditional HEMS in the state.

Given the resource implications of using HEMS, robust service data collection is essential
to investigate HEMS efficiencies and patient impact. As part of our findings, we identified
several limitations in the service databases which can be addressed to improve the
validity of future studies. This included omitted variables and the internal validity
of collected data. In terms of omitted variables; we were unable to calculate HEMS
times relative to the time of injury, however our results still provide an accurate
reflection of the time taken to complete HEMS missions from activation. Data limitations
also precluded the identification of patient entrapment which would have extended
scene time in certain cases. Internal validity is also an issue in the data we report;
as we were unable to verify data accuracy. Variables such as patient diagnosis and
clinical condition rely on the opinions of multiple clinicians which may include inconsistencies.
To address this issue, future database reconfiguration could consider linkage to hospital
trauma registries to gain more accurate information on patient diagnosis, injury severity
and outcomes.

Conclusion

Assessing the benefit of HEMS in primary scene responses is difficult as HEMS encompasses
a ‘package’ of interventions including improved access, speed and advanced clinical
skills and decision making, which is known to vary between regions. Describing the
characteristics of HEMS missions and patients in the local environment is an important
step in understanding how HEMS benefit the health system. Our results document the
time HEMS take to activate, respond to the scene, treat and transport patients as
well as the proximity of HEMS operations to the receiving hospital and the clinical
characteristics of patients attended. The high proportion of hospital bypass is a
seldom mentioned benefit of HEMS and this finding has implications for future studies
assessing the true benefit a HEMS provides. Importantly, our results highlight many
areas for future research to ensure HEMS are used efficiently and appropriately.

Abbreviations

Competing interests

The authors declare that they have no competing interests.

Authors’ contributions

CT conceived this study, carried out the statistical analysis and drafted the original
manuscript. BL provided assistance with statistical analysis and reviewed the manuscript.
EB undertook geographical mapping and reviewed the manuscript. BB, SJ and JM provided
clinical and health service expertise and reviewed the manuscript. All authors read
and approved the final manuscript.

Acknowledgments

The authors would like to acknowledge the contribution of staff in the Greater Sydney
Area HEMS who collected the data presented in this manuscript.